Contrast Enhancement of Digital Images Using an Improved Type-II Fuzzy Set-Based Algorithm

Abstract

Contrast is a distinctive image feature that tells if it has adequate visual quality or not. On many occasions, images are captured with low-contrast due to inevitable obstacles. Therefore, an improved type-II fuzzy set-based algorithm is developed to enhance the contrast of various color and grayscale images properly while preserving the brightness and providing natural colors. The proposed algorithm utilizes new upper and lower ranges, amended Hamacher t-conorm, and a transform-based gamma correction method to provide the enhanced images. The proposed algorithm is assessed with artificial and real contrast distorted images, compared with twelve specialized methods, and the outcomes are evaluated using four advanced metrics. From the obtained results of experiments and comparisons, the developed algorithm demonstrated the ability to process various color and grayscale images, performed the best among the comparative methods, and scored the best in all four quality evaluation metrics. The findings of this study are significant because the proposed algorithm has low-complexity and can adjust the contrast of different images expeditiously, which enables it to be used with different imaging modalities especially those with limited hardware resources or produce high-resolution images.

Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

“…Land believes that the color information obtained by the visual system is only related to the reflection essence of the object, but has nothing to do with the light intensity on the surface of the object [14]. e theory divides the image into two parts: one is the lowfrequency part of the image, which corresponds to the brightness component of the object; the second is the highfrequency part of the image, which corresponds to the reflection component of the object surface [15]. Among the various improved algorithms based on Retinex theory, aiming at the different estimation methods of incident light components, they mainly include Retinex algorithm of random walk, Retinex algorithm based on iterative path, and Retinex algorithm of center/surround.…”

Bilateral tone mapping scheme for color correction and contrast adjustment in nearly invisible medical images

“…This section explains the in‐depth subjective and objective analysis of the proposed approach with the existing enhancement approaches. The recent enhancement algorithms used for comparison are CLAHE, 3 AGCWD, 4 EGIF, 17 MAMBF, 11 BIMEF, 20 Type II, 7 CEEF, 12 PCARM, 21 EMST, 2 and DPFRM 22 . The color medical images used for experimentation are collected from the standard benchmark datasets such as Kvasir‐SEG, 24 NeoPolyp, 25 computer‐assisted diagnosis for CAPsule endoscopy (CAD‐CAP), 26 Kvasir‐Capsule, 27 and retinal fundus dataset 21 .…”

“…Many researchers adopted fuzzy logic and channel adjustment schemes to improve the clinical information of medical images. Ameen presented an enhanced Type‐II fuzzy set model to improve the contrast of both grayscale and color images while conserving brightness 7 . The proposed system exploits new lower and upper ranges, a transform‐based gamma correction method, and a modified fuzzy set to generate the processed images.…”

“…Ameen presented an enhanced Type-II fuzzy set model to improve the contrast of both grayscale and color images while conserving brightness. 7 The proposed system exploits new lower and upper ranges, a transform-based gamma correction method, and a modified fuzzy set to generate the processed images. Cao and Li developed a detail-richest-channel method for improving the quality of photographed retinal images by a handheld device.…”

Comparison of Image Enhancement Algorithms for Improving the Visual Quality in Computer Vision Application

“…Then, apply the Type-II fuzzy membership function are determined with the lower and upper ranges of the Hamacher t-conorm, where, α is a parameter that controls the amount of contrast enhancement, in that it should satisfy 0 < α ≤ 1, when α > 0.6, better contrast enhancement is obtained [20]. An improved type-II fuzzy set (IT2FS) algorithm [21], using Fuzzified image followed by Hamacher t-conorm method and then finally applying Gamma Correction The enhanced output of Improved Type-II fuzzy set-based algorithm with different α values is as shown in the Fig. 4(a) When α is between 0.3.5 and 0.55, the results will be obtained with satisfactory visual quality.…”